An application of principal component analysis to the clavicle and clavicle fixation devices
© Daruwalla et al; licensee BioMed Central Ltd. 2010
Received: 5 November 2009
Accepted: 26 March 2010
Published: 26 March 2010
Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories.
Materials and methods
Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation.
The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups.
Discussion And Conclusions
This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.
The selection of any orthopaedic fixation implant is driven by several factors. However, the shape of the bone involved is commonly overlooked. When selecting a clavicular implant, there are several factors that drive the decision but the morphology of the clavicle is rarely considered. Experience to date has shown that linear scaling is a dominant mode of variation in human anatomy . This paper builds on geometric data and methodology presented in a previous study analyzing linear measurements  in order to provide detailed information relating to the modes of variation in three-dimensional (3D) shape that occur in the clavicle. It must be noted that while intramedullary and plate fixation are accepted and widely used methods of treatment for fractures of the clavicle, current clavicular implants overlook the variations in geometry of the bone. As the clavicle demonstrates a complex anatomy, it is vital to understand the variations not only in size but also shape. This allows optimization of the implant design, in turn ensuring effective fracture fixation. This is the first 3D study that examines the shape variation of the clavicle and suggests a method of grouping the clavicle into size and shape categories based on statistical shape and principal component analyses.
Ethics approval for this study was sought and granted through the Royal College of Surgeons in Ireland Research Ethics Committee (Study No. REC 401). Fifteen fresh frozen shoulder specimens previously used for a shoulder course and consented for research purposes were scanned using high-resolution (0.625 mm) computerized tomography (CT). These specimens were stored in a freezer compartment in airtight bags for two months after the course and defrosted for 24 hours prior to being scanned. Surrounding soft tissue was not removed. One clavicle was found to be fractured, and five were incomplete so were excluded. A further 16 high-resolution CT scans of the clavicle were obtained by searching the hospital database but four were excluded because they did not include either the superior or inferior medial or lateral aspects completely. In order to ensure none of the clavicles had pathology, search criteria included patients who had a CT scan performed for imaging of the proximal humerus or scapula. The study comprised a total of 21 clavicles.
Six of the scans were from males and 15 from females, with an average age of 54 (range 20-85 years). Twelve were from the left side and nine from the right. Biodata was available in all cases and cause of death in the group of fresh frozen specimens was known. None of the 21 clavicles scanned showed signs of a previous fracture.
All CT scans were reconstructed using Mimics software (Materialise b.v., Leuven, Belgium). These images were subsequently imported as three-dimensional (3D) STL files into Arthron, a statistical software package specifically developed by the Department of Mechanical Engineering in the institution where our research was being conducted.
Clavicular Coordinate Frame
Statistical Shape Modelling of the Clavicle
Using the corresponding surface landmarks, a statistical model of clavicle form was generated using Point Distribution Modelling (PDM) . The PDM technique represents a training set of landmark data using the mean landmarks and a set of eigenvectors which represent the linearly independent modes of variation (principal components) of the data set. Landmark data from the training set can be approximated using the eigenvectors corresponding to the largest eigenvalues λ i . New models can also be generated by transforming the mean shape using the linearly scaled combinations of the most significant eigenvectors. By applying a scaling limit of the shapes generated will be similar to those in the original training set. Unlike the approach taken by Cooper et al , the subject models were not normalised by size hence the PDM included both size and shape variation.
Results of the principal component analysis (PCA) comprised of size and shape components. A size component reflects the variation in dimensions purely due to size, with the ratios between dimensions remaining constant while the actual values of the dimensions change. This is identifiable as a principal component (PC) whose coefficients are of the same sign and similar magnitude. Other PCs show variation in the shape of the clavicle which is due to changing ratios between dimensions, irrespective of size. Two clavicles are defined to be the same shape if scaling, rotating and translating allows them to occupy the same space.
Cluster analysis is a technique used to categorize objects into groups that share similar characteristics. Using the k-means function from the MATLAB® Statistics Toolbox , the clavicles were sorted into groups based on their PC values. The correct numbers of clusters were determined by iterively varying k until the sum of the mean Euclidean distance between each data point and the centroid of the neighbouring clusters was maximized. Local minima were avoided by performing the clustering procedure with several thousand replicates.
Mean and standard deviations of linear measurements.
Relationship between principal components and linear measurements.
Discussion and Conclusions
The application of principal component analysis (PCA) allows the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past, examples including the femur  and knee . However, PCA of the clavicle has not been performed.
Using PCA, interrelated variables are separated into sets of linearly independent equations . As no statistically significant differences were observed between principal components when comparing sides, our study focused purely on gender-specific differences. By analyzing PC values between men and women, it is clearly seen that PC1 and PC4 are gender-related. The difference in the mean values of these PCs indicates that men generally have longer clavicles that are thicker and wider at their midpoints. These features are also found to demonstrate greater variance in men. PC3, which represents the sternal depth and angle, also indicates gender-related differences with men again exhibiting greater variance. Less significant gender-related difference was noted in PC2, which represents the acromial depth and angle.
By using k-means clustering, the clavicles were also grouped on a size basis using PC1 and on a size and shape basis with all four PCs. The silhouette value  of a clustered data point is a measure of how similar that point is to points in its own cluster compared to points in other clusters. The optimal number of clusters was determined by varying k so the mean silhouette value of the clustered data was minimized. Unlike a study in 2008 which stated that three types of modern human clavicles exist , our k-means clustering results suggest the possibility of at least five morphological groups, each composed solely of a single gender. However, it must be stated that our findings were based on a limited number of clavicles and that an increased number would be more desirable in order to support the presence of the five morphological groups we describe.
- Fitzpatrick C, Fitzpatrick D, Auger D, Lee J: A tibial-based coordinate system for three-dimensional data. Knee. 2007, 14 (2): 133-137. 10.1016/j.knee.2006.11.001.View ArticlePubMedGoogle Scholar
- Daruwalla ZJ, Courtis P, Fitzpatrick C, Fitzpatrick D, Mullett H: Anatomic Variation of the Clavicle. A Novel Three-Dimensional Study. Clin Anat. 2010, 23 (2): 199-209.PubMedGoogle Scholar
- Cooper DH, Cootes TF, Taylor CJ, Graham J: Active shape models - their training and application. Computer Vision and Image Understanding. 1995, 61 (1): 38-59. 10.1006/cviu.1995.1004.View ArticleGoogle Scholar
- MathWorks. Natick, Massachusetts. 2007Google Scholar
- Fleute M, Lavallee S: Bulding a complete surface model from sparse data using statistical shape models: Applications to computer assisted knee surgery. Medical Image Computing and Computer-Assisted Intervention. 1998, 1496: 879-887.Google Scholar
- Stindel E, Briard J, Merloz P, Plaweski S, Dubrana F, Lefevre C, Troccaz J: Bone morphing: 3d morphological data for total knee arthroplasty. Computer Aided Surgery. 2002, 7 (3): 156-168. 10.3109/10929080209146026.View ArticlePubMedGoogle Scholar
- Jolliffe IT: Principal Component Analysis. New York, SpringerGoogle Scholar
- Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. 1990, Hoboken, NJ: John Wiley & Sons, Inc, 1986-View ArticleGoogle Scholar
- Voisin JL: The Omo I clavicle: Archaic or modern?. J Hum Evol. 2008, 55 (3): 438-43. 10.1016/j.jhevol.2008.06.001.View ArticlePubMedGoogle Scholar
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